Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권규현 | - |
dc.date.accessioned | 2022-04-22T05:27:44Z | - |
dc.date.available | 2022-04-22T05:27:44Z | - |
dc.date.issued | 2020-08 | - |
dc.identifier.citation | BRAIN SCIENCES, v. 10, no. 8, article no. 512 | en_US |
dc.identifier.issn | 2076-3425 | - |
dc.identifier.uri | https://www.mdpi.com/2076-3425/10/8/512 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170217 | - |
dc.description.abstract | Sensorimotor rhythm (SMR)-based brain-computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented. | en_US |
dc.description.sponsorship | This work was supported in part by the National Science Foundation (NSF) under Grant Number IIS-1421948. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | brain–computer interface (BCI) | en_US |
dc.subject | functional electrical stimulation (FES) | en_US |
dc.subject | sensorimotor rhythm (SMR) | en_US |
dc.subject | adaptive learning | en_US |
dc.subject | rehabilitation | en_US |
dc.title | Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation | en_US |
dc.type | Article | en_US |
dc.relation.no | 8 | - |
dc.relation.volume | 10 | - |
dc.identifier.doi | 10.3390/brainsci10080512 | - |
dc.relation.page | 1-27 | - |
dc.relation.journal | BRAIN SCIENCES | - |
dc.contributor.googleauthor | Choi, Inchul | - |
dc.contributor.googleauthor | Kwon, Gyu Hyun | - |
dc.contributor.googleauthor | Lee, Sangwon | - |
dc.contributor.googleauthor | Nam, Chang S. | - |
dc.relation.code | 2020048334 | - |
dc.sector.campus | S | - |
dc.sector.daehak | GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S] | - |
dc.sector.department | DEPARTMENT OF TECHNOLOGY MANAGEMENT | - |
dc.identifier.pid | ghkwon | - |
dc.identifier.orcid | https://orcid.org/0000-0003-1623-4867 | - |
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